path_proj = here::here()
path_source = file.path(path_proj, "source")
source(file.path(path_source, "simulation", "simulations_functions_final.R"))
##
## Attaching package: 'tidyr'
## The following object is masked from 'package:reshape2':
##
## smiths
source(file.path(path_source, "functions", "plot_function.R"))
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
source(file.path(path_source, "functions", "fit_function.R"))
source(file.path(path_source, "functions", "table_function.R"))
# place for draws
# mac
posterior_draws_path = file.path(Sys.getenv("HOME"), "Desktop", "draws", "testEach")
#Windows
#posterior_draws_path = file.path(Sys.getenv("USERPROFILE"), "Desktop", "draws", "testEach")
#data path
data_save_path = file.path(path_proj, "data", "fitted_model", "simulation", "3. b_rw")
#models
q_constant <- file.path(path_proj, "source", "models",
"q-constant.stan")
b_constant <- file.path(path_proj, "source", "models",
"b-constant.stan")
b_rw <- file.path(path_proj, "source", "models",
"b-rw1.stan")
b_ou <- file.path(path_proj, "source", "models",
"b-ou.stan")
compiled_models <- list(
q_constant = cmdstan_model(q_constant),
b_constant = cmdstan_model(b_constant),
b_rw = cmdstan_model(b_rw),
b_ou = cmdstan_model(b_ou)
)
models_to_use <- c("q_constant", "b_constant", "b_rw", "b_ou")
simulate data - FR
setting
###### setting #####
seed <- 123
set.seed(seed)
# data
alpha_increase_seq_1 <- seq(10, 750, by = 30)
alpha_decrease_seq_1 <- seq(750, 10, by = -30)
alpha_lamb = c( rep(10,5), alpha_increase_seq_1 + rnorm(alpha_increase_seq_1,10,10),
alpha_decrease_seq_1 + rnorm(alpha_decrease_seq_1,10,10),
rep(10,5))
beta_lamb = 0.5
T = 60
# reprot delay
D <- 15;
# Time period for checking
D_check <- 5
first_date <- as.Date("2024-01-01")
scoreRange <- c(first_date+days(9), first_date+days(19), first_date+days(29),
first_date+days(39), first_date+days(49))
params_b_rw_FR <- list(
data = list(
alpha_lamb = alpha_lamb,
beta_lamb = beta_lamb,
T = T,
date_start = as.Date("2024-01-01"),
D = D
),
q_model = list(
method = "b_rw",
method_params = list(mu_logb = log(0.7), sigma_logb = 0.1, mu_logitphi = 1, sigma_logitphi = 0.1)
)
)
b_rw_FR <- simulateData(params_b_rw_FR)
par(mfrow = c(2, 1))
plot(b_rw_FR$b, pch = 19, type = "b")
plot(b_rw_FR$phi, pch = 19, type = "b")
par(mfrow = c(1, 1))
matplot(t(b_rw_FR$q), type = "l", lty = 1, ylim = c(0, 1))
params_b_rw_NFR <- list(
data = list(
alpha_lamb = alpha_lamb,
beta_lamb = beta_lamb,
T = T,
date_start = as.Date("2024-01-01"),
D = D
),
q_model = list(
method = "b_rw",
method_params = list(mu_logb = log(0.1), sigma_logb = 0.1, mu_logitphi = 1.5,
sigma_logitphi = 0.1)
)
)
b_rw_NFR <- simulateData(params_b_rw_NFR)
par(mfrow = c(2, 1))
plot(b_rw_NFR$b, pch = 19, type = "b")
plot(b_rw_NFR$phi, pch = 19, type = "b")
par(mfrow = c(1, 1))
matplot(t(b_rw_NFR$q), type = "l", lty = 1, ylim = c(0, 1))
b_rw_NFR$case_true - b_rw_NFR$case_reported_cumulated[,16]
## [,1]
## 2024-01-01 7
## 2024-01-02 2
## 2024-01-03 6
## 2024-01-04 4
## 2024-01-05 6
## 2024-01-06 6
## 2024-01-07 20
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## 2024-01-10 46
## 2024-01-11 54
## 2024-01-12 38
## 2024-01-13 97
## 2024-01-14 112
## 2024-01-15 122
## 2024-01-16 131
## 2024-01-17 131
## 2024-01-18 139
## 2024-01-19 87
## 2024-01-20 101
## 2024-01-21 136
## 2024-01-22 116
## 2024-01-23 138
## 2024-01-24 158
## 2024-01-25 190
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## 2024-01-27 232
## 2024-01-28 248
## 2024-01-29 309
## 2024-01-30 332
## 2024-01-31 298
## 2024-02-01 213
## 2024-02-02 218
## 2024-02-03 234
## 2024-02-04 257
## 2024-02-05 227
## 2024-02-06 207
## 2024-02-07 185
## 2024-02-08 183
## 2024-02-09 213
## 2024-02-10 179
## 2024-02-11 161
## 2024-02-12 135
## 2024-02-13 123
## 2024-02-14 127
## 2024-02-15 95
## 2024-02-16 117
## 2024-02-17 75
## 2024-02-18 67
## 2024-02-19 49
## 2024-02-20 37
## 2024-02-21 40
## 2024-02-22 39
## 2024-02-23 32
## 2024-02-24 15
## 2024-02-25 8
## 2024-02-26 15
## 2024-02-27 6
## 2024-02-28 8
## 2024-02-29 5
expplot
# exploritary analysis
page_num <- ceiling(nrow(b_rw_FR$case_reported_cumulated)/16)
exp_plot_b_rw <- fit_exp_plot(b_rw_FR$case_reported_cumulated,ncol = 4, nrow = 4, page = c(1:page_num), if_fit = T)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
print(exp_plot_b_rw)
## $plots
## $plots[[1]]
##
## $plots[[2]]
##
## $plots[[3]]
##
## $plots[[4]]
##
##
## $coefficients
## b phi
## 1 0.5102478 0.3242183
## 2 0.3653369 0.3084090
## 3 0.4476095 0.3421965
## 4 0.4490649 0.2918580
## 5 0.3397700 0.2939834
## 6 0.6011245 0.3748400
## 7 0.4381524 0.2885234
## 8 0.5476037 0.2856854
## 9 0.5017568 0.3719762
## 10 0.5369134 0.2670427
## 11 0.5729134 0.2276394
## 12 0.5371611 0.2855857
## 13 0.5941961 0.2708847
## 14 0.4387457 0.2990528
## 15 0.7278341 0.2707825
## 16 0.6597422 0.2946573
## 17 0.6511569 0.2539173
## 18 0.6853104 0.2809225
## 19 0.6583041 0.2745087
## 20 0.7350582 0.2385135
## 21 0.7132630 0.2361698
## 22 0.7545682 0.2105783
## 23 0.7576714 0.2360144
## 24 0.7388811 0.2399718
## 25 0.9318239 0.2407887
## 26 0.8858472 0.2032041
## 27 0.7703753 0.2613836
## 28 0.7229013 0.2708974
## 29 0.7746562 0.2797567
## 30 0.8035316 0.2488242
## 31 0.8080676 0.2804276
## 32 0.7148676 0.2288080
## 33 0.8190414 0.2289338
## 34 0.7366797 0.1885100
## 35 0.7052928 0.1987711
## 36 0.7118669 0.1923962
## 37 0.7637729 0.2392929
## 38 0.7788860 0.2183033
## 39 0.7442910 0.2543408
## 40 0.8062958 0.2661258
## 41 0.7907656 0.2748617
## 42 0.8044802 0.2104328
## 43 0.7381683 0.2923139
## 44 0.8673423 0.3002924
## 45 0.7035423 0.2677609
## 46 0.6335863 0.3116783
## 47 0.7580283 0.3187835
## 48 0.8201927 0.2894523
## 49 0.6957779 0.2782749
## 50 0.6578685 0.3586028
## 51 0.6607782 0.3176169
## 52 0.8178736 0.2767404
## 53 0.9253342 0.2464679
## 54 0.8543887 0.2472600
## 55 0.7458717 0.1894083
## 56 0.6579016 0.1592701
## 57 1.0154663 0.1695938
## 58 1.2701982 0.3523995
## 59 0.9695121 0.2565201
## 60 0.7668025 0.3984388
exp_plot_b_rw$coefficients
## b phi
## 1 0.5102478 0.3242183
## 2 0.3653369 0.3084090
## 3 0.4476095 0.3421965
## 4 0.4490649 0.2918580
## 5 0.3397700 0.2939834
## 6 0.6011245 0.3748400
## 7 0.4381524 0.2885234
## 8 0.5476037 0.2856854
## 9 0.5017568 0.3719762
## 10 0.5369134 0.2670427
## 11 0.5729134 0.2276394
## 12 0.5371611 0.2855857
## 13 0.5941961 0.2708847
## 14 0.4387457 0.2990528
## 15 0.7278341 0.2707825
## 16 0.6597422 0.2946573
## 17 0.6511569 0.2539173
## 18 0.6853104 0.2809225
## 19 0.6583041 0.2745087
## 20 0.7350582 0.2385135
## 21 0.7132630 0.2361698
## 22 0.7545682 0.2105783
## 23 0.7576714 0.2360144
## 24 0.7388811 0.2399718
## 25 0.9318239 0.2407887
## 26 0.8858472 0.2032041
## 27 0.7703753 0.2613836
## 28 0.7229013 0.2708974
## 29 0.7746562 0.2797567
## 30 0.8035316 0.2488242
## 31 0.8080676 0.2804276
## 32 0.7148676 0.2288080
## 33 0.8190414 0.2289338
## 34 0.7366797 0.1885100
## 35 0.7052928 0.1987711
## 36 0.7118669 0.1923962
## 37 0.7637729 0.2392929
## 38 0.7788860 0.2183033
## 39 0.7442910 0.2543408
## 40 0.8062958 0.2661258
## 41 0.7907656 0.2748617
## 42 0.8044802 0.2104328
## 43 0.7381683 0.2923139
## 44 0.8673423 0.3002924
## 45 0.7035423 0.2677609
## 46 0.6335863 0.3116783
## 47 0.7580283 0.3187835
## 48 0.8201927 0.2894523
## 49 0.6957779 0.2782749
## 50 0.6578685 0.3586028
## 51 0.6607782 0.3176169
## 52 0.8178736 0.2767404
## 53 0.9253342 0.2464679
## 54 0.8543887 0.2472600
## 55 0.7458717 0.1894083
## 56 0.6579016 0.1592701
## 57 1.0154663 0.1695938
## 58 1.2701982 0.3523995
## 59 0.9695121 0.2565201
## 60 0.7668025 0.3984388
exp_b_data_b_rw<- data.frame( date = as.Date(rownames(b_rw_FR$case_reported_cumulated)),
b = exp_plot_b_rw$coefficients$b)
exp_b_plot_b_rw <- ggplot(exp_b_data_b_rw, aes(x = date, y = b)) +
geom_point(color = "black", size = 1.5) +
geom_smooth(method = "loess", se = TRUE,
color = "blue", fill = "grey", alpha = 0.5) +
theme_minimal() +
labs(x = NULL, y = "Y", title = "Smoothed Curve of parameter b")
print(exp_b_plot_b_rw)
## `geom_smooth()` using formula = 'y ~ x'
# exploritary analysis
page_num <- ceiling(nrow(b_rw_NFR$case_reported_cumulated)/16)
exp_plot_b_rw <- fit_exp_plot(b_rw_NFR$case_reported_cumulated,ncol = 4, nrow = 4, page = c(1:page_num), if_fit = T)
print(exp_plot_b_rw)
## $plots
## $plots[[1]]
##
## $plots[[2]]
##
## $plots[[3]]
##
## $plots[[4]]
##
##
## $coefficients
## b phi
## 1 0.23910127 0.12731033
## 2 0.14307222 0.21207681
## 3 0.18282214 0.12125292
## 4 0.15070194 0.19235510
## 5 0.11672660 0.06986817
## 6 0.19065752 0.17170494
## 7 0.17034141 0.13607374
## 8 0.19247986 0.12220650
## 9 0.17726165 0.09267310
## 10 0.18056943 0.12851609
## 11 0.17190178 0.09483770
## 12 0.18583349 0.11371414
## 13 0.17700723 0.12314865
## 14 0.15689921 0.16709594
## 15 0.14470621 0.11190978
## 16 0.17416584 0.14863184
## 17 0.16389252 0.14095565
## 18 0.15679565 0.17812054
## 19 0.18654569 0.15977487
## 20 0.18081863 0.18148340
## 21 0.18161363 0.21429750
## 22 0.17699920 0.24996216
## 23 0.16964826 0.27218221
## 24 0.17033615 0.25333376
## 25 0.16720046 0.20141935
## 26 0.18549233 0.21639904
## 27 0.16696982 0.19372895
## 28 0.17166586 0.18913271
## 29 0.16957894 0.18463813
## 30 0.15138569 0.19223931
## 31 0.16359092 0.19633644
## 32 0.17575780 0.18140011
## 33 0.17005360 0.18259659
## 34 0.16321524 0.15329901
## 35 0.16124535 0.20477246
## 36 0.16082623 0.24208419
## 37 0.17004578 0.19251882
## 38 0.17189117 0.22746632
## 39 0.16578062 0.21047873
## 40 0.16699487 0.21502090
## 41 0.15850437 0.19739285
## 42 0.16961976 0.22191056
## 43 0.17177557 0.23944822
## 44 0.17098530 0.22533894
## 45 0.16254020 0.21983684
## 46 0.17096671 0.21714553
## 47 0.15942756 0.27243277
## 48 0.17190734 0.22264551
## 49 0.18305043 0.24037747
## 50 0.17497658 0.21235222
## 51 0.16323330 0.28818886
## 52 0.18255363 0.25834212
## 53 0.16006506 0.32805788
## 54 0.18024980 0.26376189
## 55 0.21130160 0.33282115
## 56 0.12760549 0.18362417
## 57 0.17370331 0.07956690
## 58 0.15300945 0.29171182
## 59 0.08145321 0.09746539
## 60 0.17688469 0.18686621
exp_plot_b_rw$coefficients
## b phi
## 1 0.23910127 0.12731033
## 2 0.14307222 0.21207681
## 3 0.18282214 0.12125292
## 4 0.15070194 0.19235510
## 5 0.11672660 0.06986817
## 6 0.19065752 0.17170494
## 7 0.17034141 0.13607374
## 8 0.19247986 0.12220650
## 9 0.17726165 0.09267310
## 10 0.18056943 0.12851609
## 11 0.17190178 0.09483770
## 12 0.18583349 0.11371414
## 13 0.17700723 0.12314865
## 14 0.15689921 0.16709594
## 15 0.14470621 0.11190978
## 16 0.17416584 0.14863184
## 17 0.16389252 0.14095565
## 18 0.15679565 0.17812054
## 19 0.18654569 0.15977487
## 20 0.18081863 0.18148340
## 21 0.18161363 0.21429750
## 22 0.17699920 0.24996216
## 23 0.16964826 0.27218221
## 24 0.17033615 0.25333376
## 25 0.16720046 0.20141935
## 26 0.18549233 0.21639904
## 27 0.16696982 0.19372895
## 28 0.17166586 0.18913271
## 29 0.16957894 0.18463813
## 30 0.15138569 0.19223931
## 31 0.16359092 0.19633644
## 32 0.17575780 0.18140011
## 33 0.17005360 0.18259659
## 34 0.16321524 0.15329901
## 35 0.16124535 0.20477246
## 36 0.16082623 0.24208419
## 37 0.17004578 0.19251882
## 38 0.17189117 0.22746632
## 39 0.16578062 0.21047873
## 40 0.16699487 0.21502090
## 41 0.15850437 0.19739285
## 42 0.16961976 0.22191056
## 43 0.17177557 0.23944822
## 44 0.17098530 0.22533894
## 45 0.16254020 0.21983684
## 46 0.17096671 0.21714553
## 47 0.15942756 0.27243277
## 48 0.17190734 0.22264551
## 49 0.18305043 0.24037747
## 50 0.17497658 0.21235222
## 51 0.16323330 0.28818886
## 52 0.18255363 0.25834212
## 53 0.16006506 0.32805788
## 54 0.18024980 0.26376189
## 55 0.21130160 0.33282115
## 56 0.12760549 0.18362417
## 57 0.17370331 0.07956690
## 58 0.15300945 0.29171182
## 59 0.08145321 0.09746539
## 60 0.17688469 0.18686621
exp_b_data_b_rw<- data.frame( date = as.Date(rownames(b_rw_NFR$case_reported_cumulated)),
b = exp_plot_b_rw$coefficients$b)
exp_b_plot_b_rw <- ggplot(exp_b_data_b_rw, aes(x = date, y = b)) +
geom_point(color = "black", size = 1.5) +
geom_smooth(method = "loess", se = TRUE,
color = "blue", fill = "grey", alpha = 0.5) +
theme_minimal() +
labs(x = NULL, y = "Y", title = "Smoothed Curve of parameter b")
print(exp_b_plot_b_rw)
## `geom_smooth()` using formula = 'y ~ x'
fit model
out_b_rw_FR <-
nowcasting_moving_window(b_rw_FR$case_reported_cumulated, scoreRange = scoreRange,
case_true = b_rw_FR$case_true,
start_date = as.Date("2024-01-01"),
D = D, seeds = seed,
methods =models_to_use,
compiled_models = compiled_models,
iter_sampling = 2000, iter_warmup = 1000, refresh = 0,
num_chains = 3, suppress_output = T,
posterior_draws_path = file.path(posterior_draws_path, "b_rw")
)
## ====================
## now=2024-01-10 (1/5)
## ====================
## Warning in nowcasting_moving_window(b_rw_FR$case_reported_cumulated, scoreRange
## = scoreRange, : The number of rows of the input data is smaller than number of
## max delay D, which might cause inaccuracy.
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 0.6 seconds.
## Chain 2 finished in 0.5 seconds.
## Chain 3 finished in 0.5 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 0.6 seconds.
## Total execution time: 2.2 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 0.2 seconds.
## Chain 2 finished in 0.3 seconds.
## Chain 3 finished in 0.2 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 0.2 seconds.
## Total execution time: 1.1 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 1.3 seconds.
## Chain 2 finished in 2.2 seconds.
## Chain 3 finished in 1.7 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 1.7 seconds.
## Total execution time: 5.5 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 2.3 seconds.
## Chain 2 finished in 3.1 seconds.
## Chain 3 finished in 2.4 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 2.6 seconds.
## Total execution time: 8.2 seconds.
##
## ====================
## now=2024-01-20 (2/5)
## ====================
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 1.3 seconds.
## Chain 2 finished in 1.2 seconds.
## Chain 3 finished in 1.2 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 1.2 seconds.
## Total execution time: 4.0 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 0.4 seconds.
## Chain 2 finished in 0.4 seconds.
## Chain 3 finished in 0.3 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 0.4 seconds.
## Total execution time: 1.4 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 3.3 seconds.
## Chain 2 finished in 4.6 seconds.
## Chain 3 finished in 3.6 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 3.8 seconds.
## Total execution time: 11.7 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 4.3 seconds.
## Chain 2 finished in 4.0 seconds.
## Chain 3 finished in 3.7 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 4.0 seconds.
## Total execution time: 12.2 seconds.
##
## ====================
## now=2024-01-30 (3/5)
## ====================
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 1.7 seconds.
## Chain 2 finished in 1.6 seconds.
## Chain 3 finished in 1.5 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 1.6 seconds.
## Total execution time: 5.1 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 0.7 seconds.
## Chain 2 finished in 0.6 seconds.
## Chain 3 finished in 0.5 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 0.6 seconds.
## Total execution time: 2.1 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 4.4 seconds.
## Chain 2 finished in 4.2 seconds.
## Chain 3 finished in 4.4 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 4.3 seconds.
## Total execution time: 13.3 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 3.7 seconds.
## Chain 2 finished in 4.4 seconds.
## Chain 3 finished in 3.8 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 4.0 seconds.
## Total execution time: 12.3 seconds.
##
## ====================
## now=2024-02-09 (4/5)
## ====================
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 2.5 seconds.
## Chain 2 finished in 2.6 seconds.
## Chain 3 finished in 2.4 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 2.5 seconds.
## Total execution time: 7.7 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 0.7 seconds.
## Chain 2 finished in 0.7 seconds.
## Chain 3 finished in 0.7 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 0.7 seconds.
## Total execution time: 2.4 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 5.8 seconds.
## Chain 2 finished in 5.8 seconds.
## Chain 3 finished in 5.7 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 5.8 seconds.
## Total execution time: 17.7 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 4.7 seconds.
## Chain 2 finished in 4.7 seconds.
## Chain 3 finished in 5.4 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 4.9 seconds.
## Total execution time: 15.1 seconds.
##
## ====================
## now=2024-02-19 (5/5)
## ====================
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 3.1 seconds.
## Chain 2 finished in 2.7 seconds.
## Chain 3 finished in 3.2 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 3.0 seconds.
## Total execution time: 9.2 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 0.9 seconds.
## Chain 2 finished in 0.9 seconds.
## Chain 3 finished in 0.9 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 0.9 seconds.
## Total execution time: 3.0 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 7.4 seconds.
## Chain 2 finished in 7.8 seconds.
## Chain 3 finished in 7.6 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 7.6 seconds.
## Total execution time: 23.1 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 7.3 seconds.
## Chain 2 finished in 6.4 seconds.
## Chain 3 finished in 6.6 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 6.7 seconds.
## Total execution time: 20.6 seconds.
save(out_b_rw_FR, file = file.path(data_save_path, "FR_b_rw.RData"))
#load( file.path(data_save_path, "FR_b_rw.RData"))
results_b_rw_FR <- nowcasts_table(out_b_rw_FR, D = D, report_unit = "day",
methods = models_to_use)
plots_b_rw_FR <- nowcasts_plot(results_b_rw_FR, D = D, report_unit = "day", methods = models_to_use)
## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
for (i in 1:length(results_b_rw_FR)) {
print(calculate_metrics(results_b_rw_FR[[i]],
methods = models_to_use))
}
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 77.76 62.00 53.53 60.54 120.61 0.6 q_constant
## 2 15.48 8.05 8.07 6.66 57.31 1.0 b_constant
## 3 16.19 8.09 8.20 6.71 67.21 1.0 b_rw
## 4 16.11 8.18 8.19 6.75 61.61 1.0 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 67.30 12.43 46.87 11.57 107.16 0.75 q_constant
## 2 31.90 4.50 15.75 3.07 80.65 1.00 b_constant
## 3 27.66 3.88 12.12 2.40 95.06 1.00 b_rw
## 4 32.36 4.49 15.30 2.93 90.51 1.00 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 54.52 4.65 34.19 3.75 109.34 0.87 q_constant
## 2 30.63 2.88 15.09 2.07 103.67 0.97 b_constant
## 3 22.12 1.91 8.14 1.12 124.34 1.00 b_rw
## 4 22.88 2.04 9.28 1.27 124.95 1.00 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 24.40 2.43 15.79 1.82 114.33 1 q_constant
## 2 17.62 2.33 9.94 1.59 112.18 1 b_constant
## 3 19.45 2.14 7.97 1.10 126.29 1 b_rw
## 4 13.36 1.71 6.42 1.02 125.13 1 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 33.60 6.90 15.32 2.61 110.07 0.98 q_constant
## 2 30.66 6.63 11.12 2.32 108.13 0.98 b_constant
## 3 15.13 3.31 5.80 1.24 112.65 1.00 b_rw
## 4 19.68 4.35 6.14 1.40 113.17 1.00 b_ou
for (i in 1:length(results_b_rw_FR)) {
print(calculate_metrics(data.table::last(results_b_rw_FR[[i]],D_check),
methods = models_to_use))
}
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 109.38 67.98 96.61 66.08 211.23 0.2 q_constant
## 2 21.86 9.17 15.36 8.42 97.01 1.0 b_constant
## 3 22.86 9.31 15.58 8.40 116.41 1.0 b_rw
## 4 22.75 9.33 15.60 8.43 105.81 1.0 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 117.55 15.88 109.96 14.80 226.63 0.2 q_constant
## 2 61.62 7.75 49.53 6.40 166.02 1.0 b_constant
## 3 53.39 6.65 37.18 4.76 219.22 1.0 b_rw
## 4 62.92 7.89 49.07 6.33 202.02 1.0 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 110.77 8.05 102.23 7.52 231.61 0.4 q_constant
## 2 68.07 4.85 58.24 4.25 220.60 0.8 b_constant
## 3 52.28 3.67 34.30 2.43 337.63 1.0 b_rw
## 4 54.72 3.83 42.74 3.04 339.24 1.0 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 46.65 4.58 35.68 3.39 198.42 1 q_constant
## 2 38.92 3.77 28.06 2.64 192.42 1 b_constant
## 3 53.08 5.24 39.78 3.83 293.43 1 b_rw
## 4 35.47 3.46 28.97 2.77 289.23 1 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 97.61 21.08 62.40 13.01 140.01 0.8 q_constant
## 2 93.36 20.19 56.80 11.93 137.80 0.8 b_constant
## 3 45.81 9.84 31.14 6.40 169.40 1.0 b_rw
## 4 61.00 13.19 38.42 7.95 175.02 1.0 b_ou
print(plots_b_rw_FR)
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## [[4]]
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## [[5]]
out_b_rw_NFR <-
nowcasting_moving_window(b_rw_NFR$case_reported_cumulated, scoreRange = scoreRange,
case_true = b_rw_NFR$case_true,
start_date = as.Date("2024-01-01"),
D = D, seeds = seed,
methods =models_to_use,
compiled_models = compiled_models,
iter_sampling = 2000, iter_warmup = 1000, refresh = 0,
num_chains = 3, suppress_output = T,
posterior_draws_path = file.path(posterior_draws_path, "b_rw")
)
## ====================
## now=2024-01-10 (1/5)
## ====================
## Warning in nowcasting_moving_window(b_rw_NFR$case_reported_cumulated,
## scoreRange = scoreRange, : The number of rows of the input data is smaller than
## number of max delay D, which might cause inaccuracy.
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 0.6 seconds.
## Chain 2 finished in 0.5 seconds.
## Chain 3 finished in 0.5 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 0.5 seconds.
## Total execution time: 1.8 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 0.2 seconds.
## Chain 2 finished in 0.2 seconds.
## Chain 3 finished in 0.2 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 0.2 seconds.
## Total execution time: 1.1 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 1.7 seconds.
## Chain 2 finished in 2.6 seconds.
## Chain 3 finished in 2.3 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 2.2 seconds.
## Total execution time: 7.0 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 2.2 seconds.
## Chain 2 finished in 2.6 seconds.
## Chain 3 finished in 1.3 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 2.0 seconds.
## Total execution time: 6.3 seconds.
##
## ====================
## now=2024-01-20 (2/5)
## ====================
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 1.3 seconds.
## Chain 2 finished in 1.2 seconds.
## Chain 3 finished in 1.1 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 1.2 seconds.
## Total execution time: 3.9 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 0.8 seconds.
## Chain 2 finished in 0.7 seconds.
## Chain 3 finished in 0.8 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 0.8 seconds.
## Total execution time: 2.5 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 2.1 seconds.
## Chain 2 finished in 6.0 seconds.
## Chain 3 finished in 5.5 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 4.6 seconds.
## Total execution time: 14.0 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 4.7 seconds.
## Chain 2 finished in 4.0 seconds.
## Chain 3 finished in 5.3 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 4.7 seconds.
## Total execution time: 14.2 seconds.
##
## ====================
## now=2024-01-30 (3/5)
## ====================
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 1.4 seconds.
## Chain 2 finished in 1.3 seconds.
## Chain 3 finished in 1.3 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 1.3 seconds.
## Total execution time: 4.3 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 1.3 seconds.
## Chain 2 finished in 1.5 seconds.
## Chain 3 finished in 1.3 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 1.4 seconds.
## Total execution time: 4.4 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 9.8 seconds.
## Chain 2 finished in 11.0 seconds.
## Chain 3 finished in 9.7 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 10.2 seconds.
## Total execution time: 30.8 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 7.1 seconds.
## Chain 2 finished in 16.7 seconds.
## Chain 3 finished in 9.3 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 11.0 seconds.
## Total execution time: 33.3 seconds.
##
## ====================
## now=2024-02-09 (4/5)
## ====================
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 2.1 seconds.
## Chain 2 finished in 1.9 seconds.
## Chain 3 finished in 1.7 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 1.9 seconds.
## Total execution time: 6.1 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 1.9 seconds.
## Chain 2 finished in 1.3 seconds.
## Chain 3 finished in 1.2 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 1.5 seconds.
## Total execution time: 4.7 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 13.4 seconds.
## Chain 2 finished in 11.5 seconds.
## Chain 3 finished in 13.8 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 12.9 seconds.
## Total execution time: 39.0 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 9.6 seconds.
## Chain 2 finished in 7.9 seconds.
## Chain 3 finished in 7.4 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 8.3 seconds.
## Total execution time: 25.1 seconds.
##
## ====================
## now=2024-02-19 (5/5)
## ====================
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 2.1 seconds.
## Chain 2 finished in 2.0 seconds.
## Chain 3 finished in 1.9 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 2.0 seconds.
## Total execution time: 6.3 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 1.8 seconds.
## Chain 2 finished in 1.6 seconds.
## Chain 3 finished in 2.8 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 2.0 seconds.
## Total execution time: 6.5 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 26.3 seconds.
## Chain 2 finished in 17.4 seconds.
## Chain 3 finished in 19.5 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 21.1 seconds.
## Total execution time: 63.5 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 13.2 seconds.
## Chain 2 finished in 9.8 seconds.
## Chain 3 finished in 12.2 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 11.7 seconds.
## Total execution time: 35.6 seconds.
save(out_b_rw_NFR, file = file.path(data_save_path, "FR_b_rw.RData"))
#load( file.path(data_save_path, "FR_b_rw.RData"))
results_b_rw_NFR <- nowcasts_table(out_b_rw_NFR, D = D, report_unit = "day",
methods = models_to_use)
plots_b_rw_NFR <- nowcasts_plot(results_b_rw_NFR, D = D, report_unit = "day", methods = models_to_use)
for (i in 1:length(results_b_rw_NFR)) {
print(calculate_metrics(results_b_rw_NFR[[i]],
methods = models_to_use))
}
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 14.16 16.62 8.58 11.47 93.01 1.0 q_constant
## 2 64.62 47.77 45.08 46.96 50.71 0.3 b_constant
## 3 68.84 49.35 47.62 48.51 55.21 0.2 b_rw
## 4 67.94 49.48 47.24 48.69 51.41 0.2 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 66.32 19.06 46.68 16.43 108.71 0.65 q_constant
## 2 57.70 12.68 32.79 8.96 126.31 0.95 b_constant
## 3 41.77 14.66 26.41 10.79 200.06 0.95 b_rw
## 4 57.99 14.73 35.33 10.81 166.76 0.95 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 94.94 16.79 66.40 13.93 110.17 0.53 q_constant
## 2 144.04 15.96 96.20 13.46 150.02 0.63 b_constant
## 3 113.92 12.26 57.00 8.11 234.92 0.97 b_rw
## 4 68.15 9.78 41.96 6.93 213.45 1.00 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 166.19 19.54 132.10 18.04 109.43 0.20 q_constant
## 2 63.96 9.37 47.07 6.86 126.53 0.82 b_constant
## 3 67.79 9.13 45.02 6.35 198.72 0.98 b_rw
## 4 58.65 8.39 37.98 5.69 181.21 0.98 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 155.69 18.48 125.62 17.15 102.29 0.20 q_constant
## 2 49.65 8.36 35.78 5.90 117.85 0.80 b_constant
## 3 35.97 7.46 25.93 4.73 158.80 0.98 b_rw
## 4 36.47 6.97 26.47 4.53 153.99 0.98 b_ou
for (i in 1:length(results_b_rw_NFR)) {
print(calculate_metrics(data.table::last(results_b_rw_NFR[[i]],D_check),
methods = models_to_use))
}
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 19.59 9.98 14.03 7.69 159.42 1.0 q_constant
## 2 90.71 47.77 79.36 47.61 84.81 0.2 b_constant
## 3 96.71 50.55 84.40 50.40 93.82 0.0 b_rw
## 4 95.42 50.19 83.46 50.05 86.41 0.0 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 79.97 11.55 68.84 9.75 243.82 0.6 q_constant
## 2 97.91 12.01 78.80 10.02 283.42 1.0 b_constant
## 3 45.17 6.06 37.79 5.11 546.41 1.0 b_rw
## 4 77.77 11.18 64.56 9.20 423.63 1.0 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 172.73 12.55 151.99 11.08 238.83 0.2 q_constant
## 2 202.00 15.91 179.99 13.82 326.43 0.4 b_constant
## 3 267.52 19.45 241.98 17.71 655.44 0.8 b_rw
## 4 151.98 11.06 138.26 10.18 586.63 1.0 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 141.15 12.43 134.23 11.84 204.00 0.4 q_constant
## 2 72.05 6.35 67.93 5.99 236.82 1.0 b_constant
## 3 88.39 8.08 79.34 7.19 518.02 1.0 b_rw
## 4 55.66 4.99 42.24 3.79 434.85 1.0 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 48.31 9.79 45.40 9.45 126.20 0.8 q_constant
## 2 46.17 9.88 43.76 9.40 149.01 1.0 b_constant
## 3 17.62 4.03 15.39 3.43 249.83 1.0 b_rw
## 4 14.10 2.94 11.04 2.29 215.83 1.0 b_ou
print(plots_b_rw_NFR)
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